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  • 2019-01-11

Cloud-based semi-automated liver segmentation: analytical study to compare its speed and accuracy with a semi-automated workstation based software

Aims and objectives

Living Donor Liver Transplantation (LDLT) is the commonest form of liver transplantation in Asia. While Deceasead Donor Liver Transplantation (DDLT) constitutes more than 90% of liver transplantation in the western world, in India and many other Asian countries, the majority of transplants performed are LDLT.

Living Donor Liver Transplants is now a well established procedure and has reduced liver transplant waiting list mortality. The complexities of the procedure, along with the donor risks, are the biggest obstacles to widespread use of this valuable treatment option. Pre-operative surgical evaluation and preparation is perhaps the single most important determinant of successful outcomes for both donors and recipients.



Methods and materials

Data Preparation


A test dataset of 15 multi-phasic contrast-enhanced CT scans was provided by Centre for Advanced Research in Imaging, Neurosciences and Genomics (CARING), New Delhi, India. 


 


Exclusion Criteria entailed:


Morphologic features of cirrhosis, 

History of prior liver/biliary surgery or liver tumor ablation procedures 

One or more liver lesions greater than 3 cm in size identified by CT or MRI

Portal or hepatic vein thrombosis. 

All studies were acquired on a 128-MDCT GE Discovery IQ scanner. The images were acquired using a matrix size of 512 x 512 pixels, at an in-plane pixel size of 0.76 mm, reconstructing 0.6 mm thin images.  Individual contrast bolus-tracking was performed during repetitive low dose acquisitions at 120 kVp /40 mAs and placement of a threshold region-of-interest (ROI) within the abdominal aorta at the level of the diaphragm, plotting HU contrast wash-in to a level of 150 HU following contrast administration of 100 ml 320 mg I/ml contrast agent administered at 4 ml / sec injected into a right antecubital vein using a CTA injector. The diagnostic arterial and portal-venous cranio-caudal helical hepatic MDCT acquisition commenced 12 seconds and 60 seconds post 150 HU wash-in, respectively.



Results

Consistency in Liver Volumes


In Fig. 4, we can see the volumes (in ml) obtained for the two setups. The liver volumes for the studies are consistent over the two setups with a maximum variation of 2.3% and an average variation of 0.9%.


 

Duration of Segmentation : Manual vs Automated


In Fig. 3, we can see the time taken (in mins) for performing liver volumetry on the two setups. Automation of liver volumetry accelerated the post processing significantly. Automated Liver volumetry on PredibleLiver takes an average of 3.5 minutes compared to 14.6 minutes on Commercial CT Volume Viewer.



Conclusion

The study shows that liver volumetry post processing can be significantly accelerated by initializing with Deep Learning based segmentation. We compared two setups : (1) Commercial CT Volume Viewer and (2) PredibleLiver. We found PredibleLiver to require lesser time in performing volumetric assessment over 15 studies as the segmentations come pre-initalized using Deep Learning.

Link to complete publication here: https://scholar.google.com/citations?view_op=view_citation&hl=en&user=dqpMNRUAAAAJ&cstart=20&pagesize=80&citation_for_view=dqpMNRUAAAAJ:Tyk-4Ss8FVUC

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